Colin Wilson, of Johns Hopkins, will give the following talk at the department colloquium on Friday, December 2 at 3:30 in Machmer E-37.

Bayesian inference for constraint-based phonology

Bayesian mathematics and associated algorithms provide a general solution to the problem of inferring structure from incomplete, ambiguous, and noisy data. In this talk, I apply these methods to the specific problem of learning constraint-based grammars of phonology, focusing in particular on the relative roles of the likelihood -- which depends on the language-specific sound pattern -- and the prior -- which embodies assumptions made by the learner independently of the data. Previous research has proposed a rich set of prior assumptions (e.g., that inputs are identical to outputs in early phonological learning, and that certain classes of constraints are biased to be higher-ranked), which are (approximately) enforced by an increasingly complex battery of learning mechanisms. I argue that the prior can be greatly simplified, perhaps even made completely unbiased, by embracing the learner's uncertainty about the inputs and weightings/rankings that underly the observable data. Formal analysis of an unbiased learner, together with simulations from an implementation that uses Gibbs sampling, suggest that a rich prior is not needed to ensure 'restrictiveness' or other empirically-motivated properties of phonological learning.